Wu, H., Zhang, X., Zhou, Y., Blanchet, J., Suo, Z., & Lu, T. (2023). Detection and reduction of systematic bias in high-throughput rupture experiments. Journal of the Mechanics and Physics of Solids, 174, 105249. https://doi.org/10.1016/j.jmps.2023.105249
Abstract
Some high-throughput experiments aim to test many samples simultaneously under nominally the same conditions. However, whether a particular high-throughput experiment does so must be certified. We previously described a high-throughput experiment to study the statistics of rupture stretch of materials. In such an experiment, a large set of samples were tested simultaneously. We noticed a systematic bias that samples in different subsets give different statistical distributions of rupture stretch. Here we describe an approach to detect and reduce systematic bias in the high-throughput experiment. We divide the whole set of the data of the experiment into subsets, obtain the statistical distribution of rupture stretches for each subset, and compare the “closeness” of the distributions among the pairs of subsets by using the Anderson-Darling (A-D) test. We then try to reduce the systematic bias by improving the …
Authors
Hou Wu, Xuhui Zhang, Yifan Zhou, Jose Blanchet, Zhigang Suo, Tongqing Lu
Publication date
2023/5/1
Journal
Journal of the Mechanics and Physics of Solids
Volume
174
Pages
105249
Publisher
Pergamon
